122 lines
4.4 KiB
Python
Executable File
122 lines
4.4 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from .locality_aware_nms import nms_locality
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import cv2
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class EASTPostPocess(object):
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"""
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The post process for EAST.
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"""
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def __init__(self, params):
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self.score_thresh = params['score_thresh']
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self.cover_thresh = params['cover_thresh']
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self.nms_thresh = params['nms_thresh']
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def restore_rectangle_quad(self, origin, geometry):
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"""
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Restore rectangle from quadrangle.
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"""
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# quad
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origin_concat = np.concatenate(
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(origin, origin, origin, origin), axis=1) # (n, 8)
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pred_quads = origin_concat - geometry
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pred_quads = pred_quads.reshape((-1, 4, 2)) # (n, 4, 2)
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return pred_quads
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def detect(self,
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score_map,
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geo_map,
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score_thresh=0.8,
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cover_thresh=0.1,
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nms_thresh=0.2):
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"""
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restore text boxes from score map and geo map
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"""
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score_map = score_map[0]
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geo_map = np.swapaxes(geo_map, 1, 0)
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geo_map = np.swapaxes(geo_map, 1, 2)
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# filter the score map
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xy_text = np.argwhere(score_map > score_thresh)
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if len(xy_text) == 0:
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return []
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# sort the text boxes via the y axis
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xy_text = xy_text[np.argsort(xy_text[:, 0])]
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#restore quad proposals
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text_box_restored = self.restore_rectangle_quad(
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xy_text[:, ::-1] * 4, geo_map[xy_text[:, 0], xy_text[:, 1], :])
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boxes = np.zeros((text_box_restored.shape[0], 9), dtype=np.float32)
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boxes[:, :8] = text_box_restored.reshape((-1, 8))
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boxes[:, 8] = score_map[xy_text[:, 0], xy_text[:, 1]]
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boxes = nms_locality(boxes.astype(np.float64), nms_thresh)
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if boxes.shape[0] == 0:
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return []
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# Here we filter some low score boxes by the average score map,
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# this is different from the orginal paper.
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for i, box in enumerate(boxes):
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mask = np.zeros_like(score_map, dtype=np.uint8)
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cv2.fillPoly(mask, box[:8].reshape(
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(-1, 4, 2)).astype(np.int32) // 4, 1)
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boxes[i, 8] = cv2.mean(score_map, mask)[0]
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boxes = boxes[boxes[:, 8] > cover_thresh]
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return boxes
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def sort_poly(self, p):
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"""
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Sort polygons.
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"""
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min_axis = np.argmin(np.sum(p, axis=1))
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p = p[[min_axis, (min_axis + 1) % 4,\
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(min_axis + 2) % 4, (min_axis + 3) % 4]]
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if abs(p[0, 0] - p[1, 0]) > abs(p[0, 1] - p[1, 1]):
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return p
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else:
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return p[[0, 3, 2, 1]]
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def __call__(self, outs_dict, ratio_list):
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score_list = outs_dict['f_score']
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geo_list = outs_dict['f_geo']
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img_num = len(ratio_list)
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dt_boxes_list = []
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for ino in range(img_num):
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score = score_list[ino]
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geo = geo_list[ino]
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boxes = self.detect(
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score_map=score,
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geo_map=geo,
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score_thresh=self.score_thresh,
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cover_thresh=self.cover_thresh,
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nms_thresh=self.nms_thresh)
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boxes_norm = []
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if len(boxes) > 0:
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ratio_h, ratio_w = ratio_list[ino]
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boxes = boxes[:, :8].reshape((-1, 4, 2))
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boxes[:, :, 0] /= ratio_w
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boxes[:, :, 1] /= ratio_h
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for i_box, box in enumerate(boxes):
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box = self.sort_poly(box.astype(np.int32))
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if np.linalg.norm(box[0] - box[1]) < 5 \
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or np.linalg.norm(box[3] - box[0]) < 5:
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continue
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boxes_norm.append(box)
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dt_boxes_list.append(np.array(boxes_norm))
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return dt_boxes_list
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